OpenRaiser/Intern-Atlas huggingface dataset:
A Framework for Creating Natural Language User Interfaces for Action-Based Applications
Venue: arXiv cs.CL 2004
Year: 2004

In this paper we present a framework for creating natural language interfaces to action-based applications. Our framework uses a number of reusable application-independent components, in order to reduce the effort of creating a natural language interface for a given application. Using a type-logical grammar, we first translate natural language sentences into expressions in an extended higher-order logic. These expressions can be seen as executable specifications corresponding to the original sentences. The executable specifications are then interpreted by invoking appropriate procedures provided by the application for which a natural language interface is being created.
Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming
Venue: arXiv cs.AI 2004
Year: 2004

The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. The study of ant colonies behavior and their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others. In this paper, we propose an ant clustering algorithm to discover Web usage patterns (data clusters) and a linear genetic programming approach to analyze the visitor trends. Empirical results clearly shows that ant colony clustering performs well when compared to a self-organizing map (for clustering Web usage patterns) even though the performance accuracy is not that efficient when comparared to evolutionary-fuzzy clustering (i-miner) approach. KEYWORDS: Web Usage Mining, Swarm Intelligence, Ant Systems, Stigmergy, Data-Mining, Linear Genetic Programming.
Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning
Venue: arXiv cs.AI 2004
Year: 2004

Social insect societies and more specifically ant colonies, are distributed systems that, in spite of the simplicity of their individuals, present a highly structured social organization. As a result of this organization, ant colonies can accomplish complex tasks that in some cases exceed the individual capabilities of a single ant. The study of ant colonies behavior and of their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization which are useful to solve difficult optimization, classification, and distributed control problems, among others. In the present work we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we present a novel strategy to tackle unsupervised clustering as well as data retrieval problems. The present ant clustering system (ACLUSTER) avoids not only short-term memory based strategies, as well as the use of several artificial ant types (using different speeds), present in some recent approaches. Moreover and according to our knowledge, this is also the first application of ant systems into textual document clustering. KEYWORDS: Swarm Intelligence, Ant Systems, Unsupervised Clustering, Data Retrieval, Data Mining, Distributed Computing, Document Maps, Textual Document Clustering.
On the Implicit and on the Artificial - Morphogenesis and Emergent Aesthetics in Autonomous Collective Systems
Venue: arXiv cs.AI 2004
Year: 2004

Imagine a "machine" where there is no pre-commitment to any particular representational scheme: the desired behaviour is distributed and roughly specified simultaneously among many parts, but there is minimal specification of the mechanism required to generate that behaviour, i.e. the global behaviour evolves from the many relations of multiple simple behaviours. A machine that lives to and from/with Synergy. An artificial super-organism that avoids specific constraints and emerges within multiple low-level implicit bio-inspired mechanisms. KEYWORDS: Complex Science, ArtSBots Project, Swarm Intelligence, Stigmergy, UnManned Art, Symbiotic Art, Swarm Paintings, Robot Paintings, Non-Human Art, Painting Emergence and Cooperation, Art and Complexity, ArtBots: The Robot Talent Show.
Evolving Categories: Consistent Framework for Representation of Data and Algorithms
Venue: arXiv cs.DS 2004
Year: 2004

A concept of "evolving categories" is suggested to build a simple, scalable, mathematically consistent framework for representing in uniform way both data and algorithms. A state machine for executing algorithms becomes clear, rich and powerful semantics, based on category theory, and still allows easy implementation. Moreover, it gives an original insight into the nature and semantics of algorithms.
Mutual Information and Minimum Mean-square Error in Gaussian Channels
Venue: arXiv cs.IT 2004
Year: 2004

This paper deals with arbitrarily distributed finite-power input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the input-output mutual information and the minimum mean-square error (MMSE) achievable by optimal estimation of the input given the output. That is, the derivative of the mutual information (nats) with respect to the signal-to-noise ratio (SNR) is equal to half the MMSE, regardless of the input statistics. This relationship holds for both scalar and vector signals, as well as for discrete-time and continuous-time noncausal MMSE estimation. This fundamental information-theoretic result has an unexpected consequence in continuous-time nonlinear estimation: For any input signal with finite power, the causal filtering MMSE achieved at SNR is equal to the average value of the noncausal smoothing MMSE achieved with a channel whose signal-to-noise ratio is chosen uniformly distributed between 0 and SNR.
Does Meaning Evolve?
Venue: arXiv cs.CL 2004
Year: 2004

A common method of making a theory more understandable, is by comparing it to another theory which has been better developed. Radical interpretation is a theory which attempts to explain how communication has meaning. Radical interpretation is treated as another time-dependent theory and compared to the time dependent theory of biological evolution. The main reason for doing this is to find the nature of the time dependence; producing analogs between the two theories is a necessary prerequisite to this and brings up many problems. Once the nature of the time dependence is better known it might allow the underlying mechanism to be uncovered. Several similarities and differences are uncovered, there appear to be more differences than similarities.
Causes and Explanations: A Structural-Model Approach, Part I: Causes
Venue: arXiv cs.AI 2005
Year: 2005

We propose a new definition of actual cause, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account.
Phase Transitions and all that
Venue: arXiv cs.CC 2005
Year: 2005

The paper (as posted originally) contains several errors. It has been subsequently split into two papers, the corrected (and accepted for publication) versions appear in the archive as papers cs.CC/0503082 and cs.DM/0503083.
Bi-criteria Algorithm for Scheduling Jobs on Cluster Platforms
Venue: arXiv cs.DC 2005
Year: 2005

We describe in this paper a new method for building an efficient algorithm for scheduling jobs in a cluster. Jobs are considered as parallel tasks (PT) which can be scheduled on any number of processors. The main feature is to consider two criteria that are optimized together. These criteria are the makespan and the weighted minimal average completion time (minsum). They are chosen for their complementarity, to be able to represent both user-oriented objectives and system administrator objectives. We propose an algorithm based on a batch policy with increasing batch sizes, with a smart selection of jobs in each batch. This algorithm is assessed by intensive simulation results, compared to a new lower bound (obtained by a relaxation of ILP) of the optimal schedules for both criteria separately. It is currently implemented in an actual real-size cluster platform.
Statistical Machine Translation by Generalized Parsing
Venue: arXiv cs.CL 2005
Year: 2005

Designers of statistical machine translation (SMT) systems have begun to employ tree-structured translation models. Systems involving tree-structured translation models tend to be complex. This article aims to reduce the conceptual complexity of such systems, in order to make them easier to design, implement, debug, use, study, understand, explain, modify, and improve. In service of this goal, the article extends the theory of semiring parsing to arrive at a novel abstract parsing algorithm with five functional parameters: a logic, a grammar, a semiring, a search strategy, and a termination condition. The article then shows that all the common algorithms that revolve around tree-structured translation models, including hierarchical alignment, inference for parameter estimation, translation, and structured evaluation, can be derived by generalizing two of these parameters -- the grammar and the logic. The article culminates with a recipe for using such generalized parsers to train, apply, and evaluate an SMT system that is driven by tree-structured translation models.
Proportional Conflict Redistribution Rules for Information Fusion
Venue: arXiv cs.AI 2005
Year: 2005

In this paper we propose five versions of a Proportional Conflict Redistribution rule (PCR) for information fusion together with several examples. From PCR1 to PCR2, PCR3, PCR4, PCR5 one increases the complexity of the rules and also the exactitude of the redistribution of conflicting masses. PCR1 restricted from the hyper-power set to the power set and without degenerate cases gives the same result as the Weighted Average Operator (WAO) proposed recently by Jøsang, Daniel and Vannoorenberghe but does not satisfy the neutrality property of vacuous belief assignment. That's why improved PCR rules are proposed in this paper. PCR4 is an improvement of minC and Dempster's rules. The PCR rules redistribute the conflicting mass, after the conjunctive rule has been applied, proportionally with some functions depending on the masses assigned to their corresponding columns in the mass matrix. There are infinitely many ways these functions (weighting factors) can be chosen depending on the complexity one wants to deal with in specific applications and fusion systems. Any fusion combination rule is at some degree ad-hoc.
Near Optimal Routing for Small-World Networks with Augmented Local Awareness
Venue: arXiv cs.DM 2005
Year: 2005

In order to investigate the routing aspects of small-world networks, Kleinberg proposes a network model based on a $d$-dimensional lattice with long-range links chosen at random according to the $d$-harmonic distribution. Kleinberg shows that the greedy routing algorithm by using only local information performs in $O(\log^2 n)$ expected number of hops, where $n$ denotes the number of nodes in the network. Martel and Nguyen have found that the expected diameter of Kleinberg's small-world networks is $Θ(\log n)$. Thus a question arises naturally: Can we improve the routing algorithms to match the diameter of the networks while keeping the amount of information stored on each node as small as possible? We extend Kleinberg's model and add three augmented local links for each node: two of which are connected to nodes chosen randomly and uniformly within $\log^2 n$ Mahattan distance, and the third one is connected to a node chosen randomly and uniformly within $\log n$ Mahattan distance. We show that if each node is aware of $O(\log n)$ number of neighbors via the augmented local links, there exist both non-oblivious and oblivious algorithms that can route messages between any pair of nodes in $O(\log n \log \log n)$ expected number of hops, which is a near optimal routing complexity and outperforms the other related results for routing in Kleinberg's small-world networks. Our schemes keep only $O(\log^2 n)$ bits of routing information on each node, thus they are scalable with the network size. Besides adding new light to the studies of social networks, our results may also find applications in the design of large-scale distributed networks, such as peer-to-peer systems, in the same spirit of Symphony.
Utilitarian resource assignment
Venue: arXiv cs.GT 2005
Year: 2005

This paper studies a resource allocation problem introduced by Koutsoupias and Papadimitriou. The scenario is modelled as a multiple-player game in which each player selects one of a finite number of known resources. The cost to the player is the total weight of all players who choose that resource, multiplied by the ``delay'' of that resource. Recent papers have studied the Nash equilibria and social optima of this game in terms of the $L_\infty$ cost metric, in which the social cost is taken to be the maximum cost to any player. We study the $L_1$ variant of this game, in which the social cost is taken to be the sum of the costs to the individual players, rather than the maximum of these costs. We give bounds on the size of the coordination ratio, which is the ratio between the social cost incurred by selfish behavior and the optimal social cost; we also study the algorithmic problem of finding optimal (lowest-cost) assignments and Nash Equilibria. Additionally, we obtain bounds on the ratio between alternative Nash equilibria for some special cases of the problem.
Weak Typed Boehm Theorem on IMLL
Venue: arXiv cs.LO 2005
Year: 2005

In the Boehm theorem workshop on Crete island, Zoran Petric called Statman's ``Typical Ambiguity theorem'' typed Boehm theorem. Moreover, he gave a new proof of the theorem based on set-theoretical models of the simply typed lambda calculus. In this paper, we study the linear version of the typed Boehm theorem on a fragment of Intuitionistic Linear Logic. We show that in the multiplicative fragment of intuitionistic linear logic without the multiplicative unit 1 (for short IMLL) weak typed Boehm theorem holds. The system IMLL exactly corresponds to the linear lambda calculus without exponentials, additives and logical constants. The system IMLL also exactly corresponds to the free symmetric monoidal closed category without the unit object. As far as we know, our separation result is the first one with regard to these systems in a purely syntactical manner.
Similarity-Based Supervisory Control of Discrete Event Systems
Venue: arXiv cs.DM 2005
Year: 2005

Due to the appearance of uncontrollable events in discrete event systems, one may wish to replace the behavior leading to the uncontrollability of pre-specified language by some quite similar one. To capture this similarity, we introduce metric to traditional supervisory control theory and generalize the concept of original controllability to $\ld$-controllability, where $\ld$ indicates the similarity degree of two languages. A necessary and sufficient condition for a language to be $\ld$-controllable is provided. We then examine some properties of $\ld$-controllable languages and present an approach to optimizing a realization.
Multipartite Secret Correlations and Bound Information
Venue: arXiv cs.CR 2005
Year: 2005

We consider the problem of secret key extraction when $n$ honest parties and an eavesdropper share correlated information. We present a family of probability distributions and give the full characterization of its distillation properties. This formalism allows us to design a rich variety of cryptographic scenarios. In particular, we provide examples of multipartite probability distributions containing non-distillable secret correlations, also known as bound information.
Algebraic Properties for Selector Functions
Venue: arXiv cs.CC 2005
Year: 2005

The nondeterministic advice complexity of the P-selective sets is known to be exactly linear. Regarding the deterministic advice complexity of the P-selective sets--i.e., the amount of Karp--Lipton advice needed for polynomial-time machines to recognize them in general--the best current upper bound is quadratic [Ko, 1983] and the best current lower bound is linear [Hemaspaandra and Torenvliet, 1996]. We prove that every associatively P-selective set is commutatively, associatively P-selective. Using this, we establish an algebraic sufficient condition for the P-selective sets to have a linear upper bound (which thus would match the existing lower bound) on their deterministic advice complexity: If all P-selective sets are associatively P-selective then the deterministic advice complexity of the P-selective sets is linear. The weakest previously known sufficient condition was P=NP. We also establish related results for algebraic properties of, and advice complexity of, the nondeterministically selective sets.
A Logic for Non-Monotone Inductive Definitions
Venue: arXiv cs.AI 2005
Year: 2005

Well-known principles of induction include monotone induction and different sorts of non-monotone induction such as inflationary induction, induction over well-founded sets and iterated induction. In this work, we define a logic formalizing induction over well-founded sets and monotone and iterated induction. Just as the principle of positive induction has been formalized in FO(LFP), and the principle of inflationary induction has been formalized in FO(IFP), this paper formalizes the principle of iterated induction in a new logic for Non-Monotone Inductive Definitions (ID-logic). The semantics of the logic is strongly influenced by the well-founded semantics of logic programming. Our main result concerns the modularity properties of inductive definitions in ID-logic. Specifically, we formulate conditions under which a simultaneous definition $\D$ of several relations is logically equivalent to a conjunction of smaller definitions $\D_1 \land ... \land \D_n$ with disjoint sets of defined predicates. The difficulty of the result comes from the fact that predicates $P_i$ and $P_j$ defined in $\D_i$ and $\D_j$, respectively, may be mutually connected by simultaneous induction. Since logic programming and abductive logic programming under well-founded semantics are proper fragments of our logic, our modularity results are applicable there as well.
Enforcing Bulk Mail Classification
Venue: arXiv cs.NI 2005
Year: 2005

Spam costs US corporations upwards of $8.9 billion a year, and comprises as much as 40% of all email received. Solutions exist to reduce the amount of spam seen by end users, but cannot withstand sophisticated attacks. Worse yet, many will occasionally misclassify and silently drop legitimate email. Spammers take advantage of the near-zero cost of sending email to flood the network, knowing that success even a tiny fraction of the time means a profit. End users, however, have proven unwilling to pay money to send email to friends and family. We show that it is feasible to extend the existing mail system to reduce the amount of unwanted email, without misclassifying email, and without charging well-behaved users. We require that bulk email senders accurately classify each email message they send as an advertisement with an area of interest or else be charged a small negative incentive per message delivered. Recipients are able to filter out email outside their scope of interest, while senders are able to focus their sendings to the appropriate audience.
From truth to computability II
Venue: arXiv cs.LO 2005
Year: 2005

Computability logic is a formal theory of computational tasks and resources. Formulas in it represent interactive computational problems, and "truth" is understood as algorithmic solvability. Interactive computational problems, in turn, are defined as a certain sort games between a machine and its environment, with logical operators standing for operations on such games. Within the ambitious program of finding axiomatizations for incrementally rich fragments of this semantically introduced logic, the earlier article "From truth to computability I" proved soundness and completeness for system CL3, whose language has the so called parallel connectives (including negation), choice connectives, choice quantifiers, and blind quantifiers. The present paper extends that result to the significantly more expressive system CL4 with the same collection of logical operators. What makes CL4 expressive is the presence of two sorts of atoms in its language: elementary atoms, representing elementary computational problems (i.e. predicates, i.e. problems of zero degree of interactivity), and general atoms, representing arbitrary computational problems. CL4 conservatively extends CL3, with the latter being nothing but the general-atom-free fragment of the former. Removing the blind (classical) group of quantifiers from the language of CL4 is shown to yield a decidable logic despite the fact that the latter is still first-order. A comprehensive online source on computability logic can be found at http://www.cis.upenn.edu/~giorgi/cl.html
Stochastic Differential Games in a Non-Markovian Setting
Venue: arXiv cs.IT 2005
Year: 2005

Stochastic differential games are considered in a non-Markovian setting. Typically, in stochastic differential games the modulating process of the diffusion equation describing the state flow is taken to be Markovian. Then Nash equilibria or other types of solution such as Pareto equilibria are constructed using Hamilton-Jacobi-Bellman (HJB) equations. But in a non-Markovian setting the HJB method is not applicable. To examine the non-Markovian case, this paper considers the situation in which the modulating process is a fractional Brownian motion. Fractional noise calculus is used for such models to find the Nash equilibria explicitly. Although fractional Brownian motion is taken as the modulating process because of its versatility in modeling in the fields of finance and networks, the approach in this paper has the merit of being applicable to more general Gaussian stochastic differential games with only slight conceptual modifications. This work has applications in finance to stock price modeling which incorporates the effect of institutional investors, and to stochastic differential portfolio games in markets in which the stock prices follow diffusions modulated with fractional Brownian motion.